@inproceedings{b76e05a0065d4d4bb5e416a2614e1b8a,
title = "Generative Modeling Based Manifold Learning for Adaptive Filtering Guidance",
abstract = "In most practical adaptive filtering problems, estimated filters are not arbitrary, but instead lie on a manifold that encapsulates characteristics of the problem at hand. Consequently, it is desirable to steer adaptation towards filters that lie on that manifold. In this paper, we propose a novel approach to learn the manifold of a set of impulse responses and subsequently employ that learned manifold in an adaptation algorithm for system identification. The presented approach is a practical adaptive filtering recipe for enforcing a data-driven search domain constraint, instead of using conventional constrained optimization methods.",
keywords = "Manifold learning, adaptive filtering, generative models, variational autoencoder",
author = "Karim Helwani and Paris Smaragdis and Goodwin, {Michael M.}",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023 ; Conference date: 04-06-2023 Through 10-06-2023",
year = "2023",
doi = "10.1109/ICASSP49357.2023.10094985",
language = "English (US)",
series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing, Proceedings",
address = "United States",
}